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PSEA: Phenotype Set Enrichment Analysis—A New Method for Analysis of Multiple Phenotypes

Authors

  • Janina S. Ried,

    1. Institute of Genetic Epidemiology, Helmholtz Zentrum München—German Research Center for Environmental Health, Neuherberg, Germany
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  • Angela Döring,

    1. Institute of Epidemiology I, Helmholtz Zentrum München—German Research Center for Environmental Health, Neuherberg, Germany
    2. Institute of Epidemiology II, Helmholtz Zentrum München—German Research Center for Environmental Health, Neuherberg, Germany
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  • Konrad Oexle,

    1. Institute of Human Genetics, MRI, Technische Universität München, Munich, Germany
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  • Christa Meisinger,

    1. Institute of Epidemiology II, Helmholtz Zentrum München—German Research Center for Environmental Health, Neuherberg, Germany
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  • Juliane Winkelmann,

    1. Institute of Human Genetics, MRI, Technische Universität München, Munich, Germany
    2. Department of Neurology, MRI, Technische Universität München, Munich, Germany
    3. Institute of Human Genetics, Helmholtz Zentrum München—German Research Center for Environmental Health, Neuherberg, Germany
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  • Norman Klopp,

    1. Research Unit of Molecular Epidemiology, Helmholtz Zentrum München—German Research Center for Environmental Health, Neuherberg, Germany
    2. Hannover Unified Biobank, Hannover Medical School, Hannover, Germany
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  • Thomas Meitinger,

    1. Institute of Human Genetics, MRI, Technische Universität München, Munich, Germany
    2. Institute of Human Genetics, Helmholtz Zentrum München—German Research Center for Environmental Health, Neuherberg, Germany
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  • Annette Peters,

    1. Institute of Epidemiology II, Helmholtz Zentrum München—German Research Center for Environmental Health, Neuherberg, Germany
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  • Karsten Suhre,

    1. Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum München—German Research Center for Environmental Health, Neuherberg, Germany
    2. Faculty of Biology, Ludwig-Maximilians-Universität, Munich, Germany
    3. Department of Physiology and Biophysics, Weill Cornell Medical College, Doha, Qatar
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  • H.-Erich Wichmann,

    1. Institute of Epidemiology I, Helmholtz Zentrum München—German Research Center for Environmental Health, Neuherberg, Germany
    2. Institute of Medical Informatics, Biometry and Epidemiology, Chair of Epidemiology, Ludwig-Maximilians-Universität, Munich, Germany
    3. Klinikum Grosshadern, Munich, Germany
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  • Christian Gieger

    Corresponding author
    • Institute of Genetic Epidemiology, Helmholtz Zentrum München—German Research Center for Environmental Health, Neuherberg, Germany
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Correspondence to: Christian Gieger, Institute of Genetic Epidemiology, Helmholtz Zentrum München, Ingolstädter Landstraße 1, 85764 Neuherberg, Germany.

Abstract

Most genome-wide association studies (GWAS) are restricted to one phenotype, even if multiple related or unrelated phenotypes are available. However, an integrated analysis of multiple phenotypes can provide insight into their shared genetic basis and may improve the power of association studies. We present a new method, called “phenotype set enrichment analysis” (PSEA), which uses ideas of gene set enrichment analysis for the investigation of phenotype sets. PSEA combines statistics of univariate phenotype analyses and tests by permutation. It does not only allow analyzing predefined phenotype sets, but also to identify new phenotype sets. Apart from the application to situations where phenotypes and genotypes are available for each person, the method was adjusted to the analysis of GWAS summary statistics. PSEA was applied to data from the population-based cohort KORA F4 (N = 1,814) using iron-related and blood count traits. By confirming associations previously found in large meta-analyses on these traits, PSEA was shown to be a reliable tool. Many of these associations were not detectable by GWAS on single phenotypes in KORA F4. Therefore, the results suggest that PSEA can be more powerful than a single phenotype GWAS for the identification of association with multiple phenotypes. PSEA is a valuable method for analysis of multiple phenotypes, which can help to understand phenotype networks. Its flexible design enables both the use of prior knowledge and the generation of new knowledge on connection of multiple phenotypes. A software program for PSEA based on GWAS results is available upon request.

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